Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data

Description

The proposed research will enhance patient-centered outcomes research (PCOR) and comparative effectiveness research (CER) methodologies by addressing two key challenges: (1) appropriate handling of missing EHR data and (2) rigorous causal inference techniques for sequential treatment strategies. By focusing on treatment strategies tailored to individual patients and incorporating patient-reported outcomes (PROs), this study is fundamentally patient-centered. Furthermore, the research is guided by practicing physicians, a patient advocate, and a former patient caregiver, ensuring that it remains aligned with the needs and priorities of those directly affected by aNSCLC.

This study will develop novel reinforcement learning algorithms by integrating multiply robust matching-based approaches. This study will tailor each component of DTR to optimize treatment sequences for aNSCLC patients, leveraging two large-scale, high-quality nationwide real-world electronic health record (EHR) databases: the Flatiron aNSCLC database and the CancerLinQ lung cancer database. These databases provide comprehensive clinicodemographic and longitudinal patient data.

Additionally, incorporating PRO data from two National Cancer Institute (NCI)-designated Comprehensive Cancer Centers -Huntsman Cancer Institute (HCI) and Moffitt Cancer Center (MCC) – will enable this trial to capture the patient perspective when personalizing aNSCLC care recommendations. Key outcomes will include overall survival, quality-adjusted life years (QALYs), time to second progression or death (PFS2), and time to worsening of selected PROs, all framed as time-to-event outcomes.

These methodological innovations will establish a reproducible pipeline for translating real-world evidence from large-scale EHR data into personalized DTR recommendations for aNSCLC patients and other complex disease populations.